Green and Safe: An Information Sharing Scheme for Enhancing Public Safety in 5G and Beyond for Internet of Vehicles Networks
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) have long been a goal of improving urban mobility. With advances in mobile communication systems and computing technologies, these systems are becoming increasingly practical and achievable. An ITS consists of numerous components, such as vehicles, roads, traffic lights, central data hubs, roadside units, and monitoring and control centers. These components are interconnected, interact, and share information through the Internet, forming Internet of Vehicles Networks (IoVs) to optimize traffic activities. One of the most significant challenges of IoVs is ensuring public safety and security, particularly in collision warning applications and autonomous vehicle systems. Current solutions rely on cloud computing, leading to high service response times and energy consumption. To address this issue, we propose an edge-based computing architecture for 5G and beyond IoV applications. This architecture aims to reduce service response times, improve performance and energy efficiency, and reliability. Experimental results have demonstrated the effectiveness of the proposed architecture compared to existing solutions in reducing the energy consumption and carbon footprint of vehicles and urban ITS systems, paving the way for a green and safe roadmap in smart cities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".